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import argparse |
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import torch |
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from multiprocessing import cpu_count |
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class Config: |
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def __init__(self): |
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self.device = "cuda:0" |
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self.is_half = True |
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self.n_cpu = 0 |
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self.gpu_name = None |
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self.gpu_mem = None |
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( |
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self.python_cmd, |
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self.listen_port, |
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self.iscolab, |
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self.noparallel, |
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self.noautoopen, |
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self.api, |
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self.share, |
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self.files |
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) = self.arg_parse() |
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() |
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@staticmethod |
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def arg_parse() -> tuple: |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--port", type=int, default=7865, help="Listen port") |
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parser.add_argument( |
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"--pycmd", type=str, default="python", help="Python command" |
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) |
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parser.add_argument("--colab", action="store_true", help="Launch in colab") |
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parser.add_argument( |
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"--noparallel", action="store_true", help="Disable parallel processing" |
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) |
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parser.add_argument( |
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"--noautoopen", |
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action="store_true", |
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help="Do not open in browser automatically", |
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) |
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parser.add_argument('--api', action="store_true", default=False) |
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parser.add_argument("--share", action="store_true", default=False, help="share gradio app") |
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parser.add_argument("--files", action="store_true", default=False, help="load audio from path") |
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cmd_opts = parser.parse_args() |
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cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865 |
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return ( |
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cmd_opts.pycmd, |
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cmd_opts.port, |
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cmd_opts.colab, |
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cmd_opts.noparallel, |
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cmd_opts.noautoopen, |
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cmd_opts.api, |
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cmd_opts.share, |
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cmd_opts.files, |
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) |
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def device_config(self) -> tuple: |
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if torch.cuda.is_available(): |
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i_device = int(self.device.split(":")[-1]) |
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self.gpu_name = torch.cuda.get_device_name(i_device) |
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if ( |
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) |
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or "P40" in self.gpu_name.upper() |
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or "1060" in self.gpu_name |
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or "1070" in self.gpu_name |
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or "1080" in self.gpu_name |
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): |
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print("16系/10系显卡和P40强制单精度") |
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self.is_half = False |
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for config_file in ["32k.json", "40k.json", "48k.json"]: |
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with open(f"configs/{config_file}", "r") as f: |
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strr = f.read().replace("true", "false") |
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with open(f"configs/{config_file}", "w") as f: |
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f.write(strr) |
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with open("trainset_preprocess_pipeline_print.py", "r") as f: |
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strr = f.read().replace("3.7", "3.0") |
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with open("trainset_preprocess_pipeline_print.py", "w") as f: |
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f.write(strr) |
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else: |
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self.gpu_name = None |
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self.gpu_mem = int( |
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torch.cuda.get_device_properties(i_device).total_memory |
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/ 1024 |
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/ 1024 |
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/ 1024 |
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+ 0.4 |
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) |
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if self.gpu_mem <= 4: |
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with open("trainset_preprocess_pipeline_print.py", "r") as f: |
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strr = f.read().replace("3.7", "3.0") |
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with open("trainset_preprocess_pipeline_print.py", "w") as f: |
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f.write(strr) |
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elif torch.backends.mps.is_available(): |
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print("没有发现支持的N卡, 使用MPS进行推理") |
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self.device = "mps" |
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self.is_half = False |
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else: |
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print("没有发现支持的N卡, 使用CPU进行推理") |
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self.device = "cpu" |
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self.is_half = False |
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if self.n_cpu == 0: |
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self.n_cpu = cpu_count() |
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if self.is_half: |
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x_pad = 3 |
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x_query = 10 |
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x_center = 60 |
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x_max = 65 |
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else: |
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x_pad = 1 |
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x_query = 6 |
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x_center = 38 |
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x_max = 41 |
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if self.gpu_mem != None and self.gpu_mem <= 4: |
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x_pad = 1 |
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x_query = 5 |
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x_center = 30 |
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x_max = 32 |
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return x_pad, x_query, x_center, x_max |
